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Found 4 papers, 1 papers with code
Granger Causality for Compressively Sensed Sparse Signals
In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically Circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger Causality.
Cause-Effect Preservation and Classification using Neurochaos Learning
Discovering cause-effect from observational data is an important but challenging problem in science and engineering.
Learning Generalized Causal Structure in Time-series
The science of causality explains/determines 'cause-effect' relationship between the entities of a system by providing mathematical tools for the purpose.
ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification
Inspired by chaotic firing of neurons in the brain, we propose ChaosNet -- a novel chaos based artificial neural network architecture for classification tasks.
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